A maximum likelihood approach to diffeomorphic speckle tracking for 3D strain estimation in echocardiography

Curiale, Ariel Hernán; Vegas-Sánchez-Ferrero, Gonzalo; Bosch, Johan G.; Aja‐Fernández, Santiago

doi:10.1016/j.media.2015.05.001

Cited by 5 publications

(12 citation statements)

References 35 publications

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“…On the other hand, some authors proposed the use of statistical models to provide a better characterization of speckle, such as Gaussian (Cohen and Dinstein, 2002), Rayleigh (Strintzis and Kokkinidis, 1997;Cohen and Dinstein, 2002;Yue et al, 2009), Gamma (Curíale et al, 2013a), bivariate Nakagami (Myronenko et al, 2009) and bivariate Generalized Gamma (Curíale et al, 2015).…”

Section: Similarity Measurementioning

confidence: 99%

“…Besides, the metrics proposed by Myronenko et al (2009) andCuríale et al (2015) also considered a temporal correlation within the statistical model that accounts for correlation between speckle patterns throughout the deformation. These works assume that "temporal correlation leads to a better temporal tracking when statistical models of speckle are applied".…”

Section: Similarity Measurementioning

confidence: 99%

“…Instead of studying the influence of a particular improvement, we will analyze the system as a whole, similarly to what was done in De and Curíale et al (2015) for different ST methods. However, in the proposed study, we are taking into account the relation between the steps and different choices taken to implement a ST method.…”

Section: Introductionmentioning

confidence: 99%

“…4. The use of structural information into the deformation model by using the normalized convolution (Knutsson and Westin, 1993) and a maximum likelihood approach such as the one proposed in Curíale et al (2015). 5.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is important to remark that, though some registration or tracking techniques are used as a necessary step for ST, the speckle information present in US images should be coded into the similarity measure used for tracking. For instance, instead of the straightforward block matching, some authors proposed to introduce the speckle model into a Free-Form Deformation (FFD) method (Myronenko et al, 2009;Yue et al, 2009;De Craene et al, 2012;Curíale et al, 2013a), a diffeomorphic demons scheme (Curíale et al, 2015), or to introduce the monogenic phase of the B-mode image into an optical flow approach (Knutsson et al, 2005;Alessandrini et al, 2013;Tautz et al, 2013). So, according to the proposals of the state of the art for ST, one can easily distinguish three necessary and interrelated parts within a generic ST method 1.…”

Section: Introductionmentioning

confidence: 99%

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Influence of ultrasound speckle tracking strategies for motion and strain estimation

Curiale

Vegas-Sánchez-Ferrero

Aja‐Fernández

2016

Medical Image Analysis

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Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system.

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